import libraries¶

In [33]:
import numpy as np # linear alegebra
import pandas as pd # data processing ,csv i/o (eg.- pd.read_csv)
import seaborn as sns   # advance visutalisation
sns.set(style='whitegrid')
import matplotlib.pyplot as plt # visualisation
from collections import Counter
%matplotlib inline 

import os 
os.getcwd()
Out[33]:
'C:\\Users\\bibhu'
In [15]:
import warnings # it will ignore the warnings and give output
warnings.filterwarnings('ignore')
In [17]:
fifa19 = pd.read_csv(r"C:\Users\bibhu\OneDrive\Desktop\18 aug rsume proj and heart risk analysis\18th_resume project\18th_resume project\Seaborn\FIFA.csv")
In [18]:
fifa19
Out[18]:
Unnamed: 0 ID Name Age Photo Nationality Flag Overall Potential Club ... Composure Marking StandingTackle SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes Release Clause
0 0 158023 L. Messi 31 https://cdn.sofifa.org/players/4/19/158023.png Argentina https://cdn.sofifa.org/flags/52.png 94 94 FC Barcelona ... 96.0 33.0 28.0 26.0 6.0 11.0 15.0 14.0 8.0 €226.5M
1 1 20801 Cristiano Ronaldo 33 https://cdn.sofifa.org/players/4/19/20801.png Portugal https://cdn.sofifa.org/flags/38.png 94 94 Juventus ... 95.0 28.0 31.0 23.0 7.0 11.0 15.0 14.0 11.0 €127.1M
2 2 190871 Neymar Jr 26 https://cdn.sofifa.org/players/4/19/190871.png Brazil https://cdn.sofifa.org/flags/54.png 92 93 Paris Saint-Germain ... 94.0 27.0 24.0 33.0 9.0 9.0 15.0 15.0 11.0 €228.1M
3 3 193080 De Gea 27 https://cdn.sofifa.org/players/4/19/193080.png Spain https://cdn.sofifa.org/flags/45.png 91 93 Manchester United ... 68.0 15.0 21.0 13.0 90.0 85.0 87.0 88.0 94.0 €138.6M
4 4 192985 K. De Bruyne 27 https://cdn.sofifa.org/players/4/19/192985.png Belgium https://cdn.sofifa.org/flags/7.png 91 92 Manchester City ... 88.0 68.0 58.0 51.0 15.0 13.0 5.0 10.0 13.0 €196.4M
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
18202 18202 238813 J. Lundstram 19 https://cdn.sofifa.org/players/4/19/238813.png England https://cdn.sofifa.org/flags/14.png 47 65 Crewe Alexandra ... 45.0 40.0 48.0 47.0 10.0 13.0 7.0 8.0 9.0 €143K
18203 18203 243165 N. Christoffersson 19 https://cdn.sofifa.org/players/4/19/243165.png Sweden https://cdn.sofifa.org/flags/46.png 47 63 Trelleborgs FF ... 42.0 22.0 15.0 19.0 10.0 9.0 9.0 5.0 12.0 €113K
18204 18204 241638 B. Worman 16 https://cdn.sofifa.org/players/4/19/241638.png England https://cdn.sofifa.org/flags/14.png 47 67 Cambridge United ... 41.0 32.0 13.0 11.0 6.0 5.0 10.0 6.0 13.0 €165K
18205 18205 246268 D. Walker-Rice 17 https://cdn.sofifa.org/players/4/19/246268.png England https://cdn.sofifa.org/flags/14.png 47 66 Tranmere Rovers ... 46.0 20.0 25.0 27.0 14.0 6.0 14.0 8.0 9.0 €143K
18206 18206 246269 G. Nugent 16 https://cdn.sofifa.org/players/4/19/246269.png England https://cdn.sofifa.org/flags/14.png 46 66 Tranmere Rovers ... 43.0 40.0 43.0 50.0 10.0 15.0 9.0 12.0 9.0 €165K

18207 rows × 89 columns

In [19]:
fifa19.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18207 entries, 0 to 18206
Data columns (total 89 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Unnamed: 0                18207 non-null  int64  
 1   ID                        18207 non-null  int64  
 2   Name                      18207 non-null  object 
 3   Age                       18207 non-null  int64  
 4   Photo                     18207 non-null  object 
 5   Nationality               18207 non-null  object 
 6   Flag                      18207 non-null  object 
 7   Overall                   18207 non-null  int64  
 8   Potential                 18207 non-null  int64  
 9   Club                      17966 non-null  object 
 10  Club Logo                 18207 non-null  object 
 11  Value                     18207 non-null  object 
 12  Wage                      18207 non-null  object 
 13  Special                   18207 non-null  int64  
 14  Preferred Foot            18159 non-null  object 
 15  International Reputation  18159 non-null  float64
 16  Weak Foot                 18159 non-null  float64
 17  Skill Moves               18159 non-null  float64
 18  Work Rate                 18159 non-null  object 
 19  Body Type                 18159 non-null  object 
 20  Real Face                 18159 non-null  object 
 21  Position                  18147 non-null  object 
 22  Jersey Number             18147 non-null  float64
 23  Joined                    16654 non-null  object 
 24  Loaned From               1264 non-null   object 
 25  Contract Valid Until      17918 non-null  object 
 26  Height                    18159 non-null  object 
 27  Weight                    18159 non-null  object 
 28  LS                        16122 non-null  object 
 29  ST                        16122 non-null  object 
 30  RS                        16122 non-null  object 
 31  LW                        16122 non-null  object 
 32  LF                        16122 non-null  object 
 33  CF                        16122 non-null  object 
 34  RF                        16122 non-null  object 
 35  RW                        16122 non-null  object 
 36  LAM                       16122 non-null  object 
 37  CAM                       16122 non-null  object 
 38  RAM                       16122 non-null  object 
 39  LM                        16122 non-null  object 
 40  LCM                       16122 non-null  object 
 41  CM                        16122 non-null  object 
 42  RCM                       16122 non-null  object 
 43  RM                        16122 non-null  object 
 44  LWB                       16122 non-null  object 
 45  LDM                       16122 non-null  object 
 46  CDM                       16122 non-null  object 
 47  RDM                       16122 non-null  object 
 48  RWB                       16122 non-null  object 
 49  LB                        16122 non-null  object 
 50  LCB                       16122 non-null  object 
 51  CB                        16122 non-null  object 
 52  RCB                       16122 non-null  object 
 53  RB                        16122 non-null  object 
 54  Crossing                  18159 non-null  float64
 55  Finishing                 18159 non-null  float64
 56  HeadingAccuracy           18159 non-null  float64
 57  ShortPassing              18159 non-null  float64
 58  Volleys                   18159 non-null  float64
 59  Dribbling                 18159 non-null  float64
 60  Curve                     18159 non-null  float64
 61  FKAccuracy                18159 non-null  float64
 62  LongPassing               18159 non-null  float64
 63  BallControl               18159 non-null  float64
 64  Acceleration              18159 non-null  float64
 65  SprintSpeed               18159 non-null  float64
 66  Agility                   18159 non-null  float64
 67  Reactions                 18159 non-null  float64
 68  Balance                   18159 non-null  float64
 69  ShotPower                 18159 non-null  float64
 70  Jumping                   18159 non-null  float64
 71  Stamina                   18159 non-null  float64
 72  Strength                  18159 non-null  float64
 73  LongShots                 18159 non-null  float64
 74  Aggression                18159 non-null  float64
 75  Interceptions             18159 non-null  float64
 76  Positioning               18159 non-null  float64
 77  Vision                    18159 non-null  float64
 78  Penalties                 18159 non-null  float64
 79  Composure                 18159 non-null  float64
 80  Marking                   18159 non-null  float64
 81  StandingTackle            18159 non-null  float64
 82  SlidingTackle             18159 non-null  float64
 83  GKDiving                  18159 non-null  float64
 84  GKHandling                18159 non-null  float64
 85  GKKicking                 18159 non-null  float64
 86  GKPositioning             18159 non-null  float64
 87  GKReflexes                18159 non-null  float64
 88  Release Clause            16643 non-null  object 
dtypes: float64(38), int64(6), object(45)
memory usage: 12.4+ MB
In [20]:
fifa19['Body Type'].value_counts()
Out[20]:
Normal                 10595
Lean                    6417
Stocky                  1140
Messi                      1
C. Ronaldo                 1
Neymar                     1
Courtois                   1
PLAYER_BODY_TYPE_25        1
Shaqiri                    1
Akinfenwa                  1
Name: Body Type, dtype: int64
In [26]:
f, ax = plt.subplots(figsize=(8,6))
x = fifa19['Age']
ax = sns.distplot(x, bins=10)
plt.show()
In [28]:
f,ax = plt.subplots(figsize=(8,6))
x = fifa19['Age']
x =pd.Series(x , name ='Age variable')
ax =sns.distplot(x,bins=10)
plt.show()
In [35]:
f, ax = plt.subplots(figsize=(8,6))
x =fifa19['Age']
ax = sns.distplot(x,bins=10,vertical = True)
plt.show()
In [38]:
f,ax =plt.subplots(figsize=(8,6))
x = fifa19['Age']
x =pd.Series(x,name='Age varibles')
ax = sns.kdeplot(x)
plt.show()
In [41]:
f,ax = plt.subplots(figsize=(8,6))
x =fifa19['Age']
x = pd.Series(x , name ='Age variable')
ax =sns.kdeplot(x ,shade = True,color = 'g')
In [48]:
f,ax =plt.subplots(figsize=(8,6))
x = fifa19['Age']
ax = sns.distplot(x,kde = False,bins=10,rug =True)
plt.show()
In [53]:
f,ax = plt.subplots(figsize = (8,6))
x = fifa19['Age']
ax =sns.distplot(x,hist = False,bins =10,rug = True)
plt.show()
In [55]:
fifa19.columns
Out[55]:
Index(['Unnamed: 0', 'ID', 'Name', 'Age', 'Photo', 'Nationality', 'Flag',
       'Overall', 'Potential', 'Club', 'Club Logo', 'Value', 'Wage', 'Special',
       'Preferred Foot', 'International Reputation', 'Weak Foot',
       'Skill Moves', 'Work Rate', 'Body Type', 'Real Face', 'Position',
       'Jersey Number', 'Joined', 'Loaned From', 'Contract Valid Until',
       'Height', 'Weight', 'LS', 'ST', 'RS', 'LW', 'LF', 'CF', 'RF', 'RW',
       'LAM', 'CAM', 'RAM', 'LM', 'LCM', 'CM', 'RCM', 'RM', 'LWB', 'LDM',
       'CDM', 'RDM', 'RWB', 'LB', 'LCB', 'CB', 'RCB', 'RB', 'Crossing',
       'Finishing', 'HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling',
       'Curve', 'FKAccuracy', 'LongPassing', 'BallControl', 'Acceleration',
       'SprintSpeed', 'Agility', 'Reactions', 'Balance', 'ShotPower',
       'Jumping', 'Stamina', 'Strength', 'LongShots', 'Aggression',
       'Interceptions', 'Positioning', 'Vision', 'Penalties', 'Composure',
       'Marking', 'StandingTackle', 'SlidingTackle', 'GKDiving', 'GKHandling',
       'GKKicking', 'GKPositioning', 'GKReflexes', 'Release Clause'],
      dtype='object')
In [56]:
fifa19['Preferred Foot'].nunique()
Out[56]:
2
In [57]:
fifa19['Preferred Foot'].value_counts()
Out[57]:
Right    13948
Left      4211
Name: Preferred Foot, dtype: int64
In [63]:
f, ax = plt.subplots(figsize=(8,6))
sns.countplot(x ='Preferred Foot',data = fifa19,color='c')
plt.show()
In [65]:
f, ax = plt.subplots(figsize =(8,6))
sns.countplot(x ='Preferred Foot',hue = 'Real Face',data = fifa19)
plt.show()
In [69]:
f, ax = plt.subplots(figsize=(8,6))
sns.countplot(y = 'Preferred Foot',hue = 'Real Face',data = fifa19)
plt.show()
In [70]:
fifa19.columns
Out[70]:
Index(['Unnamed: 0', 'ID', 'Name', 'Age', 'Photo', 'Nationality', 'Flag',
       'Overall', 'Potential', 'Club', 'Club Logo', 'Value', 'Wage', 'Special',
       'Preferred Foot', 'International Reputation', 'Weak Foot',
       'Skill Moves', 'Work Rate', 'Body Type', 'Real Face', 'Position',
       'Jersey Number', 'Joined', 'Loaned From', 'Contract Valid Until',
       'Height', 'Weight', 'LS', 'ST', 'RS', 'LW', 'LF', 'CF', 'RF', 'RW',
       'LAM', 'CAM', 'RAM', 'LM', 'LCM', 'CM', 'RCM', 'RM', 'LWB', 'LDM',
       'CDM', 'RDM', 'RWB', 'LB', 'LCB', 'CB', 'RCB', 'RB', 'Crossing',
       'Finishing', 'HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling',
       'Curve', 'FKAccuracy', 'LongPassing', 'BallControl', 'Acceleration',
       'SprintSpeed', 'Agility', 'Reactions', 'Balance', 'ShotPower',
       'Jumping', 'Stamina', 'Strength', 'LongShots', 'Aggression',
       'Interceptions', 'Positioning', 'Vision', 'Penalties', 'Composure',
       'Marking', 'StandingTackle', 'SlidingTackle', 'GKDiving', 'GKHandling',
       'GKKicking', 'GKPositioning', 'GKReflexes', 'Release Clause'],
      dtype='object')
In [78]:
g = sns.catplot(x = 'Preferred Foot',kind = 'count',palette='ch:25',data=fifa19)
In [80]:
fifa19.columns
Out[80]:
Index(['Unnamed: 0', 'ID', 'Name', 'Age', 'Photo', 'Nationality', 'Flag',
       'Overall', 'Potential', 'Club', 'Club Logo', 'Value', 'Wage', 'Special',
       'Preferred Foot', 'International Reputation', 'Weak Foot',
       'Skill Moves', 'Work Rate', 'Body Type', 'Real Face', 'Position',
       'Jersey Number', 'Joined', 'Loaned From', 'Contract Valid Until',
       'Height', 'Weight', 'LS', 'ST', 'RS', 'LW', 'LF', 'CF', 'RF', 'RW',
       'LAM', 'CAM', 'RAM', 'LM', 'LCM', 'CM', 'RCM', 'RM', 'LWB', 'LDM',
       'CDM', 'RDM', 'RWB', 'LB', 'LCB', 'CB', 'RCB', 'RB', 'Crossing',
       'Finishing', 'HeadingAccuracy', 'ShortPassing', 'Volleys', 'Dribbling',
       'Curve', 'FKAccuracy', 'LongPassing', 'BallControl', 'Acceleration',
       'SprintSpeed', 'Agility', 'Reactions', 'Balance', 'ShotPower',
       'Jumping', 'Stamina', 'Strength', 'LongShots', 'Aggression',
       'Interceptions', 'Positioning', 'Vision', 'Penalties', 'Composure',
       'Marking', 'StandingTackle', 'SlidingTackle', 'GKDiving', 'GKHandling',
       'GKKicking', 'GKPositioning', 'GKReflexes', 'Release Clause'],
      dtype='object')
In [81]:
fifa19['International Reputation'].nunique()
Out[81]:
5
In [82]:
fifa19['International Reputation'].value_counts()
Out[82]:
1.0    16532
2.0     1261
3.0      309
4.0       51
5.0        6
Name: International Reputation, dtype: int64
In [83]:
f,ax = plt.subplots(figsize=(8,6))
sns.stripplot(x = 'International Reputation',y ='Potential',data = fifa19)
plt.show()
In [84]:
f,ax = plt.subplots(figsize = (8,6))
sns.stripplot(x = 'International Reputation',y='Potential',data = fifa19,jitter=0.01)
plt.show()
In [87]:
f,ax = plt.subplots(figsize =(8,6))
sns.stripplot(x ='International Reputation',y='Potential',\
             hue='Preferred Foot',data=fifa19,jitter=0.1,palette='Set2',\
             dodge = True)
plt.show()
In [92]:
f, ax = plt.subplots(figsize=(8,6))
sns.stripplot(x='International Reputation',y='Potential',\
             hue='Preferred Foot',data = fifa19,palette='Set2',size=20,\
              marker = 's',edgecolor='gray',alpha=.25)
plt.show()
In [94]:
f, ax = plt.subplots(figsize=(8,6))
sns.boxplot(x=fifa19['Potential'])
plt.show()
In [95]:
f,ax = plt.subplots(figsize=(8,6))
sns.boxplot(x="International Reputation",y='Potential',data=fifa19)
plt.show()
In [96]:
f,ax = plt.subplots(figsize=(8,6))
sns.boxplot(x='International Reputation',y='Potential',hue='Preferred Foot',\
           data=fifa19,palette='Set3')
plt.show()
In [98]:
f,ax=plt.subplots(figsize=(8,6))
sns.violinplot(x=fifa19['Potential'])
plt.show()
In [101]:
f,ax = plt.subplots(figsize=(8,6))
sns.violinplot(x="International Reputation",y='Potential',data=fifa19)
plt.show()
In [104]:
f,ax=plt.subplots(figsize=(8,6))
sns.violinplot(x='International Reputation',y='Potential',hue='Preferred Foot',data=fifa19,palette='muted')
plt.show()
In [107]:
f,ax=plt.subplots(figsize=(8,6))
sns.violinplot(x='International Reputation',y='Potential',\
               hue='Preferred Foot',data=fifa19,palette='muted',split=True)
Out[107]:
<Axes: xlabel='International Reputation', ylabel='Potential'>
In [108]:
f,ax= plt.subplots(figsize=(8,5))
sns.pointplot(x='International Reputation',y ='Potential',data=fifa19)
plt.show()
In [109]:
f,ax=plt.subplots(figsize=(8,6))
sns.pointplot(x='International Reputation',y ='Potential',hue = 'Preferred Foot',\
             data = fifa19,dodge=True)
plt.show()
In [111]:
f,ax = plt.subplots(figsize=(8,6))
sns.pointplot(x='International Reputation',y = 'Potential',\
             hue = 'Preferred Foot',data=fifa19,markers =['o','x'],\
             linestyles=["-","--"])
plt.show()
In [113]:
f,ax = plt.subplots(figsize=(8,6))
sns.barplot(x ='International Reputation',y='Potential',data = fifa19)
plt.show()
In [115]:
f, ax = plt.subplots(figsize=(8,6))
sns.barplot(x ='International Reputation',y='Potential',hue = 'Preferred Foot',\
           data = fifa19)
plt.show()
In [119]:
from numpy import median
f,ax = plt.subplots(figsize=(8,6))
sns.barplot(x = 'International Reputation',y = 'Potential',data = fifa19,\
           estimator = median) # estimator = median (means we can use the aggregation to determine the height of the bars)
plt.show()
In [128]:
f,ax = plt.subplots(figsize=(8,6))
sns.barplot(x = 'International Reputation',y ='Potential',data = fifa19,\
           ci=68)
plt.show()
In [129]:
f, ax = plt.subplots(figsize =(8,6))
sns.barplot(x='International Reputation',y='Potential',data=fifa19,ci='sd')
plt.show()
In [132]:
f,ax=plt.subplots(figsize=(8,6))
sns.barplot(x='International Reputation',y = 'Potential',data = fifa19,\
           capsize=1.0)
plt.show()
In [134]:
g = sns.relplot(x='Overall',y='Potential',data=fifa19)
In [136]:
f,ax = plt.subplots(figsize=(8,6))
sns.scatterplot(x='Height',y='Weight',data =fifa19)
plt.show()
In [139]:
f,ax = plt.subplots(figsize=(8,6))
sx = sns.lineplot(x='Stamina',y='Strength',data=fifa19)
plt.show()
In [140]:
f,ax = plt.subplots(figsize=(8,6))
ax = sns.regplot(x='Overall',y = 'Potential',data=fifa19)
plt.show()
In [141]:
f,ax = plt.subplots(figsize=(8,6))
ax=sns.regplot(x='Overall',y='Potential',data=fifa19,color='g',marker='+')
plt.show()
In [146]:
f,ax=plt.subplots(figsize=(8,6))
sns.regplot(x='International Reputation',y ='Potential',data=fifa19,x_jitter=0.01)
plt.show()
In [147]:
g =sns.lmplot(x='Overall',y='Potential',data=fifa19)
In [148]:
g=sns.lmplot(x='Overall',y='Potential',hue='Preferred Foot',data=fifa19)
In [150]:
g=sns.lmplot(x='Overall',y="Potential",hue='Preferred Foot',data=fifa19,palette='Set1')
In [151]:
g=sns.lmplot(x='Overall',y='Potential',col='Preferred Foot',data=fifa19)
In [152]:
g= sns.FacetGrid(fifa19,col='Preferred Foot')
In [153]:
g = sns.FacetGrid(fifa19,col='Preferred Foot')
g = g.map(plt.hist,'Potential')
In [158]:
g = sns.FacetGrid(fifa19,col='Potential')
g=(g.map(plt.hist,'Potential',bins=10,color='r'))
In [157]:
g = sns.FacetGrid(fifa19,col='Preferred Foot')
g = (g.map(plt.scatter,'Height','Weight',edgecolor="w").add_legend())
In [162]:
g = sns.FacetGrid(fifa19,col='Preferred Foot',height =5,aspect =1)
g = g.map(plt.hist,"Potential")
In [163]:
fifa19_new =fifa19[['Age','Potential','Strength','Stamina','Preferred Foot']]
In [166]:
g = sns.PairGrid(fifa19_new)
g = g.map(plt.scatter)
In [167]:
g = sns.PairGrid(fifa19_new)
g = g.map_diag(plt.hist)
g = g.map_offdiag(plt.scatter)
In [170]:
g = sns.PairGrid(fifa19_new,hue= 'Preferred Foot')
g = g.map_diag(plt.hist)
g = g.map_offdiag(plt.scatter)
g = g.add_legend()
In [171]:
g = sns.PairGrid(fifa19_new,hue = 'Preferred Foot')
g=g.map_diag(plt.hist,histtype ='step',linewidth =5)
g=g.map_offdiag(plt.scatter)
g = g.add_legend()
In [172]:
g = sns.PairGrid(fifa19_new,vars=['Age','Stamina'])
g = g.map(plt.scatter)# vars means it will create some rows
In [173]:
g = sns.PairGrid(fifa19_new)
g = g.map_upper(plt.scatter)
g = g.map_lower(sns.kdeplot,cmap = 'Blues_d')
g = g.map_diag(sns.kdeplot,lw=3,legend=False)
In [176]:
g = sns.JointGrid(x='Overall',y = 'Potential',data = fifa19)
g = g.plot(sns.regplot,sns.distplot)
In [182]:
g = sns.JointGrid(x='Overall',y = 'Potential',data=fifa19)
g= g.plot_joint(plt.scatter,color='.5',edgecolor='white') # used to joint unnivariate and bivariate in a single visualization
g = g.plot_marginals(sns.distplot,kde=False,color ='.5')
In [186]:
g = sns.JointGrid(x="Overall",y="Potential",data=fifa19,space=0)
g = g.plot_joint(sns.kdeplot,cmap=('Blues_d'))

g = g.plot_marginals(sns.kdeplot,shade=True)
In [191]:
g = sns.JointGrid(x='Overall',y ="Potential",data=fifa19,height = 5,ratio=2)
g =g.plot_joint(sns.kdeplot,cmap="Reds_d")
g = g.plot_marginals(sns.kdeplot,color="blue",shade=True)
In [192]:
f,ax=plt.subplots(figsize=(8,6))
ax = sns.regplot(x='Overall',y='Potential',data=fifa19);
In [193]:
sns.lmplot(x='Overall',y ="Potential",col='Preferred Foot',data=fifa19,\
          col_wrap=2,height=5,aspect =1)
Out[193]:
<seaborn.axisgrid.FacetGrid at 0x205ec444350>
In [196]:
def sinplot(flip=1):
    x=np.linspace(0,14,100)
    for i in range(1,7):
        plt.plot(x,np.sin(x+i * .5)*(7-i)*flip)
In [198]:
print(sinplot())

None
In [201]:
sns.set()
sinplot()
In [202]:
sns.set_style("whitegrid")
sinplot()
In [203]:
sns.set_style("dark")
sinplot()
In [204]:
sns.set_style("white")
sinplot()
In [205]:
sns.set_style('ticks')
sinplot()